{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "colab": {
      "name": "Apnea ECG Dataset analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kaustubh77/Detecting-sleep-apnea-using-CNN-on-ECG-data/blob/master/Apnea_ECG_Dataset_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLpU0FsxkxjI",
        "colab_type": "text"
      },
      "source": [
        "# Importing all libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScYGIjj6ifV2",
        "colab_type": "text"
      },
      "source": [
        "> **We need to install wfdb library for reading the .dat files that are available in the dataset.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vnl8ubQ6pJQi",
        "colab_type": "code",
        "outputId": "523c15ee-9822-428a-890a-53f6c55e1aaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        " !pip install wfdb"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wfdb in /usr/local/lib/python3.6/dist-packages (2.2.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from wfdb) (1.17.4)\n",
            "Requirement already satisfied: pandas>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from wfdb) (0.25.3)\n",
            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from wfdb) (1.3.2)\n",
            "Requirement already satisfied: requests>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from wfdb) (2.21.0)\n",
            "Requirement already satisfied: matplotlib>=1.5.1 in /usr/local/lib/python3.6/dist-packages (from wfdb) (3.1.1)\n",
            "Requirement already satisfied: nose>=1.3.7 in /usr/local/lib/python3.6/dist-packages (from wfdb) (1.3.7)\n",
            "Requirement already satisfied: sklearn>=0.0 in /usr/local/lib/python3.6/dist-packages (from wfdb) (0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->wfdb) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->wfdb) (2.6.1)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.10.0->wfdb) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.10.0->wfdb) (2019.9.11)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.10.0->wfdb) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.10.0->wfdb) (1.24.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->wfdb) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->wfdb) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->wfdb) (2.4.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn>=0.0->wfdb) (0.21.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas>=0.19.1->wfdb) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.5.1->wfdb) (41.6.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn>=0.0->wfdb) (0.14.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usHcRgQe4pKd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import wfdb\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import pickle\n",
        "import csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oN3AkvZAr2JF",
        "colab_type": "code",
        "outputId": "c90f8078-a569-455f-bdc1-5d2e182bea7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWA2CsE4sMLp",
        "colab_type": "code",
        "outputId": "9476333d-d69c-40e0-bdf7-72424e70fc3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!ls \"/content/drive/My Drive/Sleep Apnea DOP\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "annotations.csv  ecg.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyqMwS73mQyV",
        "colab_type": "text"
      },
      "source": [
        "# Some analysis of the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "qt9AuDJspJTd",
        "colab_type": "code",
        "outputId": "85f27d59-7aa2-45ef-c87f-47a676708b4f",
        "colab": {}
      },
      "source": [
        "os.listdir(\"C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\")\n",
        "\n",
        "record = wfdb.rdrecord('C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a01') \n",
        "wfdb.plot_wfdb(record, title='Record a01 from Physionet Apnea ECG') \n",
        "display(record.__dict__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'record_name': 'a01',\n",
              " 'n_sig': 1,\n",
              " 'fs': 100,\n",
              " 'counter_freq': None,\n",
              " 'base_counter': None,\n",
              " 'sig_len': 2957000,\n",
              " 'base_time': None,\n",
              " 'base_date': None,\n",
              " 'comments': [],\n",
              " 'sig_name': ['ECG'],\n",
              " 'p_signal': array([[-0.06 ],\n",
              "        [-0.065],\n",
              "        [-0.06 ],\n",
              "        ...,\n",
              "        [ 0.   ],\n",
              "        [ 0.   ],\n",
              "        [ 0.   ]]),\n",
              " 'd_signal': None,\n",
              " 'e_p_signal': None,\n",
              " 'e_d_signal': None,\n",
              " 'file_name': ['a01.dat'],\n",
              " 'fmt': ['16'],\n",
              " 'samps_per_frame': [1],\n",
              " 'skew': [None],\n",
              " 'byte_offset': [None],\n",
              " 'adc_gain': [200.0],\n",
              " 'baseline': [0],\n",
              " 'units': ['mV'],\n",
              " 'adc_res': [12],\n",
              " 'adc_zero': [0],\n",
              " 'init_value': [-12],\n",
              " 'checksum': [5827],\n",
              " 'block_size': [0]}"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jh2WlEsdpJTg",
        "colab_type": "code",
        "outputId": "05a2f11f-69c4-447f-bb3c-654253a23799",
        "colab": {}
      },
      "source": [
        "record2 = wfdb.rdrecord('a05',sampto=1000) \n",
        "wfdb.plot_wfdb(record, title='Record c10 from Physionet Apnea ECG') \n",
        "display(record2.__dict__)\n",
        "\n",
        "\n",
        "recordname = \"a04\"\n",
        "record3 = wfdb.rdsamp(recordname)\n",
        "annotation = wfdb.rdann(recordname, extension=\"apn\")\n",
        "\n",
        "annotation.contained_labels\n",
        "annotation.get_label_fields()\n",
        "annotation.symbol[:10]\n",
        "np.unique(annotation.symbol, return_counts=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              " 'base_time': None,\n",
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              "        [-0.04 ]]),\n",
              " 'd_signal': None,\n",
              " 'e_p_signal': None,\n",
              " 'e_d_signal': None,\n",
              " 'file_name': ['a05.dat'],\n",
              " 'fmt': ['16'],\n",
              " 'samps_per_frame': [1],\n",
              " 'skew': [None],\n",
              " 'byte_offset': [None],\n",
              " 'adc_gain': [200.0],\n",
              " 'baseline': [0],\n",
              " 'units': ['mV'],\n",
              " 'adc_res': [12],\n",
              " 'adc_zero': [0],\n",
              " 'init_value': [-27],\n",
              " 'checksum': [47880],\n",
              " 'block_size': [0]}"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['A', 'N'], dtype='<U1'), array([453,  39], dtype=int64))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBDoxyW6pJTj",
        "colab_type": "code",
        "outputId": "9190cd10-fb58-46c5-dac1-18e76dd7a620",
        "colab": {}
      },
      "source": [
        "record = wfdb.rdrecord(record_name='a01', sampfrom=0, sampto=1000,channels=None, physical=True, pb_dir=None, m2s=True, smooth_frames=True, ignore_skew=False, return_res=16, force_channels=True, channel_names=None, warn_empty=False)\n",
        "\n",
        "wfdb.plot_wfdb(record, title='Record a01 from Physionet Apnea ECG') \n",
        "display(record.__dict__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'record_name': 'a01',\n",
              " 'n_sig': 1,\n",
              " 'fs': 100,\n",
              " 'counter_freq': None,\n",
              " 'base_counter': None,\n",
              " 'sig_len': 1000,\n",
              " 'base_time': None,\n",
              " 'base_date': None,\n",
              " 'comments': [],\n",
              " 'sig_name': ['ECG'],\n",
              " 'p_signal': array([[-0.06 ],\n",
              "        [-0.065],\n",
              "        [-0.06 ],\n",
              "        [-0.075],\n",
              "        [-0.065],\n",
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              "        [-0.055]], dtype=float16),\n",
              " 'd_signal': None,\n",
              " 'e_p_signal': None,\n",
              " 'e_d_signal': None,\n",
              " 'file_name': ['a01.dat'],\n",
              " 'fmt': ['16'],\n",
              " 'samps_per_frame': [1],\n",
              " 'skew': [None],\n",
              " 'byte_offset': [None],\n",
              " 'adc_gain': [200.0],\n",
              " 'baseline': [0],\n",
              " 'units': ['mV'],\n",
              " 'adc_res': [12],\n",
              " 'adc_zero': [0],\n",
              " 'init_value': [-12],\n",
              " 'checksum': [65520],\n",
              " 'block_size': [0]}"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKSvDYyJpJTm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "recordname = \"a01\"\n",
        "record = wfdb.rdsamp(recordname)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fexBVXJpJTo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "annotation = wfdb.rdann(recordname, extension=\"apn\")\n",
        "annotation.contained_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5k9tVZC8pJTr",
        "colab_type": "code",
        "outputId": "1cc7cb13-917b-4071-ea69-857b954b195d",
        "colab": {}
      },
      "source": [
        "annotation.get_label_fields()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['symbol']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsQkDqtUpJTu",
        "colab_type": "code",
        "outputId": "b4ce8d9a-c015-4e43-c3f0-2c4988e5a1b6",
        "colab": {}
      },
      "source": [
        "annotation.symbol[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ijv0AyplLIZ",
        "colab_type": "text"
      },
      "source": [
        "> **We can see that we get the same number of minutes of sleep of patient a01 from both - the annotation file of patient a01 and from the ecg records data for patient a01.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofK2hqqjpJTx",
        "colab_type": "code",
        "outputId": "719f4ff4-3324-416d-a2c1-fb02f8cc36bc",
        "colab": {}
      },
      "source": [
        "len(annotation.symbol)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "489"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjkZitFPpJT0",
        "colab_type": "code",
        "outputId": "4aef8cdd-2b5f-42d0-d0ac-a126d3eb267c",
        "colab": {}
      },
      "source": [
        "record[0].shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2957000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr1q3NqIpJT3",
        "colab_type": "code",
        "outputId": "c8806b37-9517-4560-b4f8-572d695e81fc",
        "colab": {}
      },
      "source": [
        "num_seconds = record[0].shape[0]/100\n",
        "num_minutes = num_seconds/60\n",
        "print(num_minutes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "492.8333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIw-bYnuj8rD",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "> **Following few blocks of code have been commented out because they need not be executed everytime, since I have already extracted the useful data from the dataset and converted it into numpy arrays and stored it in .csv format files. If however you want to execute this code, then you must have the whole dataset ('.dat' ,'.apn','.hea','.qrs' files for all patients) in your working directory. I have stored the .csv files in my google drive and I am directly reading the .csv files from google drive. If you are executing this code in your local notebook then you must download the .csv files and store it in the current working directory and then execute.**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlPe_gI_n8vr",
        "colab_type": "text"
      },
      "source": [
        "# Saving the data in .csv files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbPV1p72pJQo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# main_pathname = \"C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\"\n",
        "# file_paths = []\n",
        "\n",
        "# for file in os.listdir(main_pathname):\n",
        "#     if file.endswith(\".dat\"):\n",
        "#         file_paths.append(os.path.join(main_pathname, file.split('.')[0]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NSR__m1pJQr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# file_paths_temp=[]\n",
        "# for file in file_paths:\n",
        "#     if(file.split('\\\\')[-1][-1]=='r'):\n",
        "#         continue\n",
        "#     file_paths_temp.append(file)\n",
        "# file_paths=file_paths_temp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cehvJ-aNpJQu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_data_files = ['C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a01',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a02',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a03',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a04',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a05',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a06',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a07',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a08',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a09',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a10',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a11',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a12',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a13',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a14',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a15',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a16',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a17',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a18',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a19',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\a20',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\b01',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\b02',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\b03',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\b04',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\b05',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c01',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c02',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c03',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c04',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c05',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c06',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c07',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c08',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c09',\n",
        "#  'C:\\\\Users\\\\KAUSTUBH\\\\Downloads\\\\DC 4-1\\\\DOP\\\\apnea-ecg-database-1.0.0\\\\c10']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bb49xRrrpJQx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(train_data_files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trBLdO7opJQz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# p_signals=[]\n",
        "\n",
        "# for file in file_paths:\n",
        "#     record = wfdb.rdrecord(file) \n",
        "#     p_signals.append(record.__dict__['p_signal'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFaaQY3VpJQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_annotations = []\n",
        "\n",
        "# for file in train_data_files:\n",
        "#     annotation = wfdb.rdann(file, extension=\"apn\")\n",
        "#     train_annotations.append(annotation.symbol[:])\n",
        "# train_annotations = np.array(train_annotations)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7N8SsvLpJQ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(train_annotations)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HOxvklSZpJQ8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# p_signals_dict={}\n",
        "# i=0\n",
        "# for file in train_data_files:\n",
        "#     record = wfdb.rdrecord(file) \n",
        "#     p_signals_dict[train_data_files[i].split('\\\\')[-1]]= record.__dict__['p_signal']\n",
        "#     i=i+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ydm6-BdDpJQ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(p_signals_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLzzo-CBpJRD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# p_signals_dict.items()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpDXfdhhpJRG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg = pd.DataFrame(list(p_signals_dict.items()))\n",
        "# ecg.columns=['name','signal']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbMvwMZrpJRJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def getlist(l):\n",
        "#     flat_list = []\n",
        "#     for sublist in l:\n",
        "#         for item in sublist:\n",
        "#             flat_list.append(item)\n",
        "#     return flat_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiNsYE4LpJRM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg['signal_list']=ecg['signal'].apply(lambda x:getlist(x))\n",
        "# ecg=pd.read_csv('ecg.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dz9eBykApJRO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg['signal'][0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbIYVNUwpJRR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJ6sIrFqpJRV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg.drop(['signal'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFs3OrropJRX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ecg.to_csv('ecg.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-Fg8PJIpJRa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.externals import joblib\n",
        "# filename = 'p_signals.sav'\n",
        "# joblib.dump(p_signals, filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFkJUockpJRd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To load the saved file\n",
        "# ecg_data = joblib.load(filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZQ5QKy1pJRg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# filename = 'train_annotations.sav'\n",
        "# joblib.dump(train_annotations, filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khxYVggDpJR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_annotations_dict={}\n",
        "# i=0\n",
        "# for file in train_data_files:\n",
        "#     annotation = wfdb.rdann(file, extension=\"apn\")\n",
        "#     train_annotations_dict[train_data_files[i].split('\\\\')[-1]] = annotation.symbol[:]\n",
        "#     i=i+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "201Z5IQRpJR6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(train_annotations_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGYE6STfpJR9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# annotations=pd.DataFrame(list(train_annotations_dict.items()))\n",
        "# annotations.columns=['name','annotation']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe_MBF0cpJSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# annotations.to_csv('annotations.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLC2aE71kLxm",
        "colab_type": "text"
      },
      "source": [
        "# Read the input the data that I have stored in the .csv format files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLGeYO2xpJRm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ecg=pd.read_csv('/content/drive/My Drive/Sleep Apnea DOP/ecg.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eud2Qz7fpJRs",
        "colab_type": "code",
        "outputId": "34a6069c-465a-486d-ff39-54ba9acc08b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# ecg.drop(['signal','Unnamed: 0'],axis=1,inplace=True)\n",
        "len(ecg['signal_list'][0].strip('][').split(', '))/6000"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "492.8333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01jAhY8zpJSD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "annot=pd.read_csv('/content/drive/My Drive/Sleep Apnea DOP/annotations.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lxm_ngBQpJSF",
        "colab_type": "code",
        "outputId": "a15ef358-cd21-4fcc-9720-7a2e327cd16f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(annot.head()['annotation'][0].strip('][').split(', '))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "489"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmB2Epv4pJSJ",
        "colab_type": "code",
        "outputId": "ef7a7b2c-66fa-4e7c-a24a-17111097afea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "i=0\n",
        "mini=1000\n",
        "maxi=0\n",
        "for i in range(35):\n",
        "    lenn=len(ecg['signal_list'][i].strip('][').split(', '))/6000\n",
        "    mini=min(mini,lenn)\n",
        "    maxi=max(maxi,lenn)\n",
        "print('Min duration: {}'.format(mini))\n",
        "print('Max duration: {}'.format(maxi))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min duration: 428.1666666666667\n",
            "Max duration: 577.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItnrwtFBpJSR",
        "colab_type": "code",
        "outputId": "ea66d8b3-b4d3-438f-930d-826b3351e3e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "i=0\n",
        "mini=1000\n",
        "maxi=0\n",
        "for i in range(35):\n",
        "    lenn=len(annot['annotation'][i].strip('][').split(', '))\n",
        "    mini=min(mini,lenn)\n",
        "    maxi=max(maxi,lenn)\n",
        "print('Min duration: {}'.format(mini))\n",
        "print('Max duration: {}'.format(maxi))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min duration: 429\n",
            "Max duration: 577\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mRO7j2mlR6a",
        "colab_type": "text"
      },
      "source": [
        "# Slicing the patient data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf42wVjspJSU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sliced_list=[]\n",
        "for i in range(35):\n",
        "    ecg_len = len(ecg['signal_list'][i].strip('][').split(', '))\n",
        "    annot_len = len(annot['annotation'][i].strip('][').split(', '))\n",
        "    n=ecg_len-annot_len\n",
        "    sliced_list.append(ecg['signal_list'][i][:len(ecg['signal_list'])-int(n)])\n",
        "# ecg['sliced_list']=ecg['signal_list'][:len(ecg['signal_list'])-int(len(ecg['signal_list'].strip('][').split(', '))-len(annot['annotation'].strip('][').split(', ')))]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYJSDmGPpJSW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# sliced_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwNsRYZ9pJSX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ecg['sliced_list']=sliced_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O541V4J9pJSZ",
        "colab_type": "code",
        "outputId": "23533f43-39de-4cd8-da66-d86e8caf0bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "ecg.drop(['Unnamed: 0','signal'],axis=1,inplace=True)\n",
        "ecg.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>signal_list</th>\n",
              "      <th>sliced_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a01</td>\n",
              "      <td>[-0.06, -0.065, -0.06, -0.075, -0.065, -0.07, ...</td>\n",
              "      <td>[-0.06, -0.065, -0.06, -0.075, -0.065, -0.07, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a02</td>\n",
              "      <td>[-0.02, -0.02, -0.025, -0.01, -0.01, -0.015, -...</td>\n",
              "      <td>[-0.02, -0.02, -0.025, -0.01, -0.01, -0.015, -...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a03</td>\n",
              "      <td>[-0.23, -0.105, 0.0, 0.0, 0.06, 0.09, 0.025, 0...</td>\n",
              "      <td>[-0.23, -0.105, 0.0, 0.0, 0.06, 0.09, 0.025, 0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a04</td>\n",
              "      <td>[-0.105, -0.13, -0.11, -0.165, -0.32, -0.21, 0...</td>\n",
              "      <td>[-0.105, -0.13, -0.11, -0.165, -0.32, -0.21, 0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a05</td>\n",
              "      <td>[-0.135, -0.185, -0.175, -0.185, -0.16, -0.185...</td>\n",
              "      <td>[-0.135, -0.185, -0.175, -0.185, -0.16, -0.185...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  name  ...                                        sliced_list\n",
              "0  a01  ...  [-0.06, -0.065, -0.06, -0.075, -0.065, -0.07, ...\n",
              "1  a02  ...  [-0.02, -0.02, -0.025, -0.01, -0.01, -0.015, -...\n",
              "2  a03  ...  [-0.23, -0.105, 0.0, 0.0, 0.06, 0.09, 0.025, 0...\n",
              "3  a04  ...  [-0.105, -0.13, -0.11, -0.165, -0.32, -0.21, 0...\n",
              "4  a05  ...  [-0.135, -0.185, -0.175, -0.185, -0.16, -0.185...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OR4It1N_pJSb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# i=0\n",
        "# for i in range(35):\n",
        "#     print(len(ecg['signal_list'][i].strip('][').split(', '))/6000)\n",
        "#     print(len(annot['annotation'][i].strip('][').split(', ')))\n",
        "#     i+=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--l7vf3kpJSe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def divide_chunks(l, n):  \n",
        "    for i in range(0, len(l), n):  \n",
        "        yield l[i:i + n] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYtVhDeRpJSg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hunks = [ecg['signal_list'][0].strip('][').split(', ')[x:x+6000] for x in range(0, len(ecg['signal_list'][0].strip('][').split(', ')), 6000)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-Cbl2xfpJTV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# annot['annotation'][0].strip('][').split(', ')[0]\n",
        "# print(\"hi {}\",format(i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDPvt6espJTY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arr = []\n",
        "for hunk in hunks:\n",
        "  arr.append(np.array(hunk))\n",
        "arr = np.array(arr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9hEOaJ-pJTa",
        "colab_type": "code",
        "outputId": "0b17a1bf-c047-4506-e07b-20798400595b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "min(np.array(annot['annotation'][0].strip('][').split(', ')).shape[0],arr.shape[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "489"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2QLN5U8mBxo",
        "colab_type": "text"
      },
      "source": [
        "# Creating X_train and Y_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vITVxsWk08n",
        "colab_type": "text"
      },
      "source": [
        "> **This part of the code takes maximum amount of time to execute.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBgyhnm2DT26",
        "colab_type": "code",
        "outputId": "9a0e8a5d-58e0-4519-ffe0-e273981d28e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "arr = []\n",
        "train_y = []\n",
        "for i in range(35):\n",
        "    print(\"Extracting patient number {}'s data \",format(i))\n",
        "    print(\"\\n\")\n",
        "    hunks = [ecg['signal_list'][i].strip('][').split(', ')[x:x+6000] for x in range(0, len(ecg['signal_list'][i].strip('][').split(', ')), 6000)]\n",
        "    mini = min(np.array(annot['annotation'][i].strip('][').split(', ')).shape[0],len(hunks))\n",
        "    for j in range(mini):\n",
        "        arr.append(np.array(hunk[j]))\n",
        "        train_y.append(annot['annotation'][i].strip('][').split(', ')[j])\n",
        "arr = np.array(arr)\n",
        "train_y = np.array(train_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting patient number {}'s data  0\n",
            "\n",
            "\n",
            "Extracting patient number {}'s data  1\n",
            "\n",
            "\n",
            "Extracting patient number {}'s data  2\n",
            "\n",
            "\n",
            "Extracting patient number {}'s data  3\n",
            "\n",
            "\n",
            "Extracting patient number {}'s data  4\n",
            "\n",
            "\n",
            "Extracting patient number {}'s data  5\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-84-b0af696b30ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Extracting patient number {}'s data \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mhunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mecg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'signal_list'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m6000\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mecg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'signal_list'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mmini\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mannot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'annotation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmini\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-84-b0af696b30ff>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Extracting patient number {}'s data \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mhunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mecg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'signal_list'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m6000\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mecg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'signal_list'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mmini\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mannot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'annotation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m']['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m', '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmini\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJgFpzYUoxZN",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGvL33jrmGci",
        "colab_type": "text"
      },
      "source": [
        "# Creating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueOxFGHDE7aL",
        "colab_type": "code",
        "outputId": "6630b7da-70c9-4f15-b9ab-9894e66a71a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dropout\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(100000,6000)))\n",
        "model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VakKMC-mJXb",
        "colab_type": "text"
      },
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uo4wOytPoJrV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "verbose, epochs, batch_size = 0, 10, 32\n",
        "model.fit(arr, trainy_, epochs=epochs, batch_size=batch_size, verbose=verbose)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}